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      1 # Copyright 2014 The Android Open Source Project
      2 #
      3 # Licensed under the Apache License, Version 2.0 (the "License");
      4 # you may not use this file except in compliance with the License.
      5 # You may obtain a copy of the License at
      6 #
      7 #      http://www.apache.org/licenses/LICENSE-2.0
      8 #
      9 # Unless required by applicable law or agreed to in writing, software
     10 # distributed under the License is distributed on an "AS IS" BASIS,
     11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     12 # See the License for the specific language governing permissions and
     13 # limitations under the License.
     14 
     15 import its.image
     16 import its.caps
     17 import its.device
     18 import its.objects
     19 import its.target
     20 import os.path
     21 import numpy
     22 
     23 def main():
     24     """Take long bursts of images and check that they're all identical.
     25 
     26     Assumes a static scene. Can be used to idenfity if there are sporadic
     27     frames that are processed differently or have artifacts. Uses manual
     28     capture settings.
     29     """
     30     NAME = os.path.basename(__file__).split(".")[0]
     31 
     32     BURST_LEN = 50
     33     BURSTS = 5
     34     FRAMES = BURST_LEN * BURSTS
     35 
     36     SPREAD_THRESH = 0.03
     37 
     38     with its.device.ItsSession() as cam:
     39 
     40         # Capture at the smallest resolution.
     41         props = cam.get_camera_properties()
     42         its.caps.skip_unless(its.caps.manual_sensor(props) and
     43                              its.caps.per_frame_control(props))
     44 
     45         _, fmt = its.objects.get_fastest_manual_capture_settings(props)
     46         e, s = its.target.get_target_exposure_combos(cam)["minSensitivity"]
     47         req = its.objects.manual_capture_request(s, e)
     48         w,h = fmt["width"], fmt["height"]
     49 
     50         # Capture bursts of YUV shots.
     51         # Get the mean values of a center patch for each.
     52         # Also build a 4D array, which is an array of all RGB images.
     53         r_means = []
     54         g_means = []
     55         b_means = []
     56         imgs = numpy.empty([FRAMES,h,w,3])
     57         for j in range(BURSTS):
     58             caps = cam.do_capture([req]*BURST_LEN, [fmt])
     59             for i,cap in enumerate(caps):
     60                 n = j*BURST_LEN + i
     61                 imgs[n] = its.image.convert_capture_to_rgb_image(cap)
     62                 tile = its.image.get_image_patch(imgs[n], 0.45, 0.45, 0.1, 0.1)
     63                 means = its.image.compute_image_means(tile)
     64                 r_means.append(means[0])
     65                 g_means.append(means[1])
     66                 b_means.append(means[2])
     67 
     68         # Dump all images.
     69         print "Dumping images"
     70         for i in range(FRAMES):
     71             its.image.write_image(imgs[i], "%s_frame%03d.jpg"%(NAME,i))
     72 
     73         # The mean image.
     74         img_mean = imgs.mean(0)
     75         its.image.write_image(img_mean, "%s_mean.jpg"%(NAME))
     76 
     77         # Pass/fail based on center patch similarity.
     78         for means in [r_means, g_means, b_means]:
     79             spread = max(means) - min(means)
     80             print spread
     81             assert(spread < SPREAD_THRESH)
     82 
     83 if __name__ == '__main__':
     84     main()
     85 
     86